Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique
Predicting learning dynamics in Multiple-Choice Decision-Making Tasks using a variational Bayes technique
复制标题
使用变分贝叶斯技术预测多项选择决策任务中的学习动态
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
U. Eden
中科院分区：
文献类型：
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作者：
A. Yousefi;Reza Kakooee;M. Beheshti;D. Dougherty;E. Eskandar;A. Widge;U. Eden
多项选择决策任务被广泛用于分析行为和推断影响决策和学习过程的潜在认知状态。在这些任务中记录的行为信号是动态的，通常是非高斯的-例如，当学习多项选择联想任务时。以前开发的潜在行为变量的估计算法不解决多项选择的反应。在这项研究中，我们使用一个状态空间建模框架来预测与多项选择决策相关的认知学习状态，这是最好的描述多项分布。该算法结合了多项式滤波器/平滑器和变分贝叶斯技术来估计学习状态向量的动态。该算法被应用到非人灵长类动物（NHP）执行多项选择决策任务的决策响应数据记录。
Multiple-Choice Decision-Making Tasks are widely used to analyze behavior and infer underlying cognitive states that shape the decision and learning processes. The behavioral signals recorded in these tasks are dynamic and often non-Gaussian - for instance, when learning a multiple choice association task. Previously developed estimation algorithms for latent behavioral variables do not address multiple-choice responses. In this research, we use a state-space modeling framework to predict a cognitive learning state related to multiple choice decisions, which are best described by a multinomial distribution. The proposed algorithm combines a multinomial filter/smoother and a variational Bayes technique to estimate the dynamics of a learning state vector. The algorithm is applied to decision response data recorded from non-human primates (NHPs) performing a Multiple-Choice Decision Task.